Travel recommendation method, electronic device, and storage medium

Abstract

A travel recommendation method, an electronic device, and a storage medium are provided, which are related to artificial intelligence, and particularly relates to fields of depth learning, map navigation and the like. The specific implementation scheme includes: obtaining a travel recommendation model according to constraint conditions and prediction conditions, wherein the constraint conditions are used for characterizing travel fairness for different types of users travelling at different moments and in different regions, and the prediction conditions are used for characterizing at least two travel modes selected by the different types of users; and obtaining travel recommendation information according to a travel target and the travel recommendation model.

What is claimed is: 
     
         1 . A travel recommendation method, comprising:
 obtaining a travel recommendation model according to constraint conditions and prediction conditions, wherein the constraint conditions are used for characterizing travel fairness for different types of users travelling at different moments and in different regions, and the prediction conditions are used for characterizing at least two travel modes selected by the different types of users; and   obtaining travel recommendation information according to a travel target and the travel recommendation model.   
     
     
         2 . The travel recommendation method of  claim 1 , wherein the constraint conditions for characterizing the travel fairness for the different types of users travelling at different moments and in different regions are associated with travel time and travel regions; and
 the prediction conditions for characterizing the at least two travel modes selected by the different types of users are associated with classifications of the at least two travel modes.   
     
     
         3 . The travel recommendation method of  claim 1 , wherein the obtaining the travel recommendation model according to the constraint conditions and the prediction conditions, wherein the constraint conditions are used for characterizing the travel fairness of the different types of users travelling at different moments and in different regions, and the prediction conditions are used for characterizing at least two travel modes selected by the different types of users, comprising:
 describing the constraint conditions for characterizing the travel fairness of the different types of users travelling at different moments and in different regions by adopting a space-time loss function;   describing the prediction conditions for characterizing the at least two travel modes selected by the different types of users by adopting a double-layer focus loss function; and   obtaining a total loss function according to the space-time loss function and the double-layer focus loss function and performing model training according to back propagation of the total loss function, to obtain the travel recommendation model.   
     
     
         4 . The travel recommendation method of  claim 2 , wherein the obtaining the travel recommendation model according to the constraint conditions and the prediction conditions, wherein the constraint conditions are used for characterizing the travel fairness of the different types of users travelling at different moments and in different regions, and the prediction conditions are used for characterizing at least two travel modes selected by the different types of users, comprising:
 describing the constraint conditions for characterizing the travel fairness of the different types of users travelling at different moments and in different regions by adopting a space-time loss function;   describing the prediction conditions for characterizing the at least two travel modes selected by the different types of users by adopting a double-layer focus loss function; and   obtaining a total loss function according to the space-time loss function and the double-layer focus loss function and performing model training according to back propagation of the total loss function, to obtain the travel recommendation model.   
     
     
         5 . The travel recommendation method of  claim 3 , further comprising:
 obtaining a temporal dimension loss function and a spatial area dimension loss function during a process of network training on a constraint network according to a first sample training set of the constraint network input in the travel recommendation model; and   obtaining the space-time loss function according to the temporal dimension loss function and the spatial area dimension loss function,   wherein the first sample training set comprises sample training data for characterizing different travel moments of different types of users and sample training data for characterizing different travel regions of the different types of users.   
     
     
         6 . The travel recommendation method of  claim 5 , further comprising:
 obtaining the temporal dimension loss function according to a predicted recommended amount at a target moment for a travel mode and an actual demand amount at the target moment for the travel mode.   
     
     
         7 . The travel recommendation method of  claim 5 , further comprising:
 obtaining the spatial area dimension loss function according to a predicted recommended amount of a target region for a travel mode and an actual demand amount of the target region for the travel mode.   
     
     
         8 . The travel recommendation method of  claim 5 , further comprising:
 constructing the travel recommendation model by acquiring output data of the constraint network, taking the output data as input data of a prediction network, and synthesizing the constraint network and the prediction network, wherein   the double-layer focus loss function is obtained during a process of network training on the prediction network.   
     
     
         9 . An electronic device, comprising:
 at least one processor; and   a memory communicatively connected to the at least one processor, wherein   the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:   obtain a travel recommendation model according to constraint conditions and prediction conditions, wherein the constraint conditions are used for characterizing travel fairness for different types of users travelling at different moments and in different regions, and the prediction conditions are used for characterizing at least two travel modes selected by the different types of users; and   obtain travel recommendation information according to a travel target and the travel recommendation model.   
     
     
         10 . The electronic device according to  claim 9 , wherein the constraint conditions for characterizing the travel fairness for the different types of users travelling at different moments and in different regions are associated with travel time and travel regions; and
 the prediction conditions for characterizing the at least two travel modes selected by the different types of users are associated with classifications of the at least two travel modes.   
     
     
         11 . The electronic device according to  claim 9 , wherein the instructions are executed by the at least one processor to further enable the at least one processor to:
 describe the constraint conditions for characterizing the travel fairness of the different types of users travelling at different moments and in different regions by adopting a space-time loss function;   describe the prediction conditions for characterizing the at least two travel modes selected by the different types of users by adopting a double-layer focus loss function; and   obtain a total loss function according to the space-time loss function and the double-layer focus loss function and perform model training according to back propagation of the total loss function, to obtain the travel recommendation model.   
     
     
         12 . The electronic device according to  claim 10 , wherein the instructions are executed by the at least one processor to further enable the at least one processor to:
 describe the constraint conditions for characterizing the travel fairness of the different types of users travelling at different moments and in different regions by adopting a space-time loss function;   describe the prediction conditions for characterizing the at least two travel modes selected by the different types of users by adopting a double-layer focus loss function; and   obtain a total loss function according to the space-time loss function and the double-layer focus loss function and perform model training according to back propagation of the total loss function, to obtain the travel recommendation model.   
     
     
         13 . The electronic device according to  claim 11 , wherein the instructions are executed by the at least one processor to further enable the at least one processor to:
 obtain a temporal dimension loss function and a spatial area dimension loss function during a process of network training on a constraint network according to a first sample training set of the constraint network input in the travel recommendation model; and   obtain the space-time loss function according to the temporal dimension loss function and the spatial area dimension loss function,   wherein the first sample training set comprises sample training data for characterizing different travel moments of different types of users and sample training data for characterizing different travel regions of the different types of users.   
     
     
         14 . The electronic device according to  claim 13 , wherein the instructions are executed by the at least one processor to further enable the at least one processor to:
 obtain the temporal dimension loss function according to a predicted recommended amount at a target moment for a travel mode and an actual demand amount at the target moment for the travel mode.   
     
     
         15 . The electronic device according to  claim 13 , wherein the instructions are executed by the at least one processor to further enable the at least one processor to:
 obtain the spatial area dimension loss function according to a predicted recommended amount of a target region for a travel mode and an actual demand amount of the target region for the travel mode.   
     
     
         16 . The electronic device according to  claim 13 , wherein the instructions are executed by the at least one processor to further enable the at least one processor to:
 construct the travel recommendation model by acquiring output data of the constraint network, take the output data as input data of a prediction network, and synthesize the constraint network and the prediction network, wherein   the double-layer focus loss function is obtained during a process of network training on the prediction network.   
     
     
         17 . A non-transitory computer-readable storage medium storing computer instructions, the computer instructions, when executed by a computer, cause the computer to:
 obtain a travel recommendation model according to constraint conditions and prediction conditions, wherein the constraint conditions are used for characterizing travel fairness for different types of users travelling at different moments and in different regions, and the prediction conditions are used for characterizing at least two travel modes selected by the different types of users; and   obtain travel recommendation information according to a travel target and the travel recommendation model.   
     
     
         18 . The non-transitory computer-readable storage medium according to  claim 17 , wherein the constraint conditions for characterizing the travel fairness for the different types of users travelling at different moments and in different regions are associated with travel time and travel regions; and
 the prediction conditions for characterizing the at least two travel modes selected by the different types of users are associated with classifications of the at least two travel modes.   
     
     
         19 . The non-transitory computer-readable storage medium according, to  claim 17 , wherein the computer instructions, when executed by a computer, further cause the computer to:
 describe the constraint conditions for characterizing the travel fairness of the different types of users travelling at different moments and in different regions by adopting a space-time loss function;   describe the prediction conditions for characterizing the at least two travel modes selected by the different types of users by adopting a double-layer focus loss function; and   obtain a total loss function according to the space-time loss function and the double-layer focus loss function and perform model training according to back propagation of the total loss function, to obtain the travel recommendation model.   
     
     
         20 . The non-transitory computer-readable storage medium according to  claim 19 , wherein the computer instructions, when executed by a computer, further cause the computer to:
 obtain a temporal dimension loss function and a spatial area dimension loss function during a process of network training on a constraint network according to a first sample training set of the constraint network input in the travel recommendation model; and   obtain the space-time loss function according to the temporal dimension loss function and the spatial area dimension loss function,   wherein the first sample training set comprises sample training data for characterizing different travel moments of different types of users and sample training data for characterizing different travel regions of the different types of users.